We find that these issues can be largely resolved by a final phase where the the feature matching process is less constrained, a similar solution to one used by Luan et al. in the image compositing [1]}. In this final phase, which we call 'Feature Splitting', matches are computed for each layer separately, resulting in \(T\)  consisting of novel hypercolumns where features at different layers are mixed and matched from different locations/rotations of the style image. Unlike [1]} we do not compute matches only once, we recompute them after every update to the output image. In this phase features are matched relative to the current output, rather than the initial content. When using our learned decoder \(\mathcal {G}\)  to synthesize, this amounts to feeding the output back into the same network as 'content' five times (recomputing \(T\)  each time). When directly optimizing the output image, this amounts to recomputing \(T\)  using the current output as the 'content' after each Adam update [3]}. For more examples of the effect of feature splitting and recomputing \(T\)  after each update, see Figure REF .
